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Aiming at the user clustering and page clustering in Web log mining and based on the analysis of K-means clustering algorithm and matrix clustering algorithm, the paper presented an improved clustering algorithm that combining fuzzy matrix algorithm with K-means algorithm. Extract compressed sub-matrix from relational matrix of user and page, establishing user interval, and then divide all users into large intervals and separate the noise data, obtain initial value and classified number for K-means algorithm, effectively solve the defect in the K-means algorithm that always suppose or make a try to definite the classified number and the initial value, also include the lacking to exclude the noise data obstruction.

Inferring dynamic complex network through a small set of samples is a challenging problem in the field of biological network, social network and transportation network, which can help improve understanding of complex network systems. In this letter, a new Hybrid Model based Latent Variables Sampling algorithm is presented to address the problems of high computation complexity and low accuracy faced by traditional approaches. Experimental results on simulated and real data sets show that the presented method possesses better reasoning performance and significantly improves the precision and efficiency of network inference especially when compared with the other three approaches. Under different dimensions, HM-LVS still has higher accuracy (average 80%) and can effectively reverse engineering dynamic complex networks from time series data.